Archive | IIoT

39

7:05 pm
February 16, 2017
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Silicon Valley Company Joins the Predictive Maintenance Party

predictive maintenance platform

Source: Element Analytics

Silicon Valley-backed Element Analytics formally announced their industrial software analytics solution, Element Platform, to the market last month. The San Francisco-based Element Analytics is taking aim at the oil and gas, chemical, utility and mining industries while partnering with OSIsoft and Microsoft’s Partner Network.

The platform and the solution is a good fit for those industries, as those fields tend to rely on proprietary automation and equipment platforms that need optimization. Oil and gas, specifically, moved their strategy from offshore to their current installed base to find profitability and most producers are understanding the need for infrastructure improvement. From the press release, the Element Platform works with OSIsoft’s technology in moving unstructured, operational sensor data from “silos” to a cloud-based analytics platform, where asset models help predict downtime for physical equipment.

Related Content | How to Start a Predictive Maintenance Program

“Industrial operators face no shortage of data, says David Mount, Kleiner Perkins’ Green Growth Fund partner and co-founder of Element Analytics. Mounds of data exist, but getting the data to a ready state is core to making it analyzable, predictive and actionable.”

Predictive maintenance technology has been slow to be adopted due to operational and production conflicts, but recent IIoT solutions live on separate platforms. This allows for control platfom updates, like security patches to occur, while not interrupting asset management programs.

The Element platform also uses Microsoft Azure and Cortana Intelligence for the cloud-based analytics.

For more information, visit www.elementanalytics.com

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

24

8:36 pm
February 9, 2017
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Analyze Big Data with Prescriptive Maintenance

By Grant Gerke, Contributing Editor

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at maintenancetechnology.com/iot.

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at maintenancetechnology.com/iot.

As manufacturers modernize plants and retrofit equipment with additional sensors, reliability and maintenance managers are working to develop Industrial Internet of Things (IIoT) strategies that effectively manage new data streams. In the past, manufacturers would employ operational consultants or specialists to analyze the mountains of data these sensors generate. Today, limited resources are driving reliability professionals to explore prescriptive maintenance.

Prescriptive maintenance is a component of the IIoT. This discipline uses machine learning and automated data review to prevent equipment or device failure. Some industry experts call it preventive maintenance with built-in intelligence.

It’s the next bridge for reliability teams to cross as referenced in the January 2017 edition of Maintenance Technology’s  On the Floor.” The focus was on regrets and hopes. One industry consultant stated, that among his clients, “the biggest regret seems to be PM/PdM compliance and not doing what they planned to do to prevent breakdowns.”

The consultant added, “that one client increased training and invested in maintenance employees but still hasn’t realized the returns on that investment.”

One reason for the lack of follow-through could be the ability to promptly act on plant-floor data, also known as perishable data in the field or factory floor.

Prescriptive maintenance allows reliability professionals to take preventive action quicker than conventional means or, in many cases, fully automates the process.

Prescriptive maintenance allows reliability professionals to take preventive action quicker than conventional means or, in many cases, fully automates the process.

In a recent article on 2017 IIoT trends, Abedayo Onigbanjo, director of marketing at Zebra Technologies, Lincolnshire, IL (zebra.com), stated “businesses must make sense of data before it expires. Enterprises are losing valuable insights with many disjointed sources generating and collecting data on their own, contributing to only bits and pieces of the big picture, instead of rendering a broad view.”

Legacy cultures and platforms are the main culprits. Take the process industries, for example. Many operations, including large chemical plants and oil fields, are relying on 4- to 20-mA fieldbus networking solutions. Identifying device defects is difficult in these facilities.

Procentec, Wateringen, The Netherlands (procentec.com), provides asset-management solutions that accelerate the identification of malfunctioning devices. In 2015, the company updated firmware for its Foundation Fieldbus Diagnostic module. The benefit was a “live list” of all operating devices in one overview, and device-type viewing in the oscilloscope images.

Documenting downtime is sometimes painful, but essential. “For a steel producer in Europe, the total estimated revenue for downtime was in the neighborhood of  [$1,600] 1,500€ a minute,” stated Matthew Dulcey, global sales manager for Procentec at a 2016 PROFI networking conference.

During the presentation, Dulcey also provided examples of how 1% of downtime for a plant running 24 hours/day equals about 78 hours of unscheduled downtime. According to Dulcey, a steel-plant maintenance team demonstrated that one preventive downtime event could pay for new diagnostic tools.

Other non-networking solutions for manufacturers include Panoramic Power’s (New York City, panpwr.com) Device Analyzer and its machine learning platform. This platform—PowerRadar version 2.0—learns usage patterns for devices in production lines and allows users to view an operational device state in real-time. The system collects device-level energy data, automatically learns device patterns and, after training, automatically identifies the different operational states of each instrument.

In the big picture for manufacturers, this is just the beginning for big data and IIoT. The road seems to lead to easy-to-use analytical tools and operational automation. MT

Grant Gerke is a business writer and content marketer in the manufacturing, power, and renewable-energy space. He has 15 years of experience covering industrial and field-automation areas.

109

3:56 pm
February 8, 2017
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How to Start a Predictive Maintenance Program

IIoT motorsDevice and equipment advances, on display in our MT IIoT web section, is past the early adoption stage, but operations and maintenance (O&M) teams are still wrapping their arms around predictive maintenance programs. A recent interview with ARC Advisory’s Ralph Rio via SAP’s Enterprise Asset Management discusses this very issue and more.

Excerpt below:

Q: So how do people begin moving toward predictive maintenance – how do they get there?

Ralph Rio: The first thing people need to do is to educate themselves to understand what is available from a technology standpoint. People just entering this area are no longer “early adopters” so there is plenty of information out there. Get comfortable with the platforms and the business processes.

Sometimes technology education is coming from your machine builder (OEM) with improved data acquisition capabilities. From this post, “Are Smaller IIoT Applications The Next Wave for End Users?” and discussion with Erl Campbell at Aventics, MT found out how this is working:

“By actually monitoring the spool position, the machine can track exactly how each valve performed during a motion cycle: where that valve started, whether it fully shifted or only partially shifted, and its final position. These data points help machine builders and end-user operators correct issues that may affect overall packaging quality and integrity,” the white paper states (written by Erl Campbell.

Campbell added in a recent interview that the company is working on whether the (valve) reliability data should communicate with the factory floor or maintenance. Is it going to be some kind of wireless communication or will techs plug into the manifold and download that data?

>> For more on how to create a predictive maintenance program with Ralph Rio

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

81

5:11 am
February 2, 2017
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Big Data Challenge as Train Company Moves to Predictive Maintenance

maintenance costs

Trenitalia, an Italian train company, looks to reduce maintenance costs by 8 percent.

There’s an interesting blog series on Trenitalia, a state-owned Italian train company, via ARC’s Industrie 4.0 website that depicts a transition from condition monitoring to a more predictive approach. The company reveals its real-time dashboards, but also discusses their transition to a component-based maintenance approach, which has many parallels to the factory or field space.

The scope is impressive. The new predictive application includes up to 4,000 “rolling stock” assets, with each locomotive collects up to 10,000 parameters per second. According to a news report, sensors will measure variables such as motor temperature, speed, traction, braking effort and line voltage.rt and line voltage.

More from the News report:

Sensor data is aggregated on-board through a remote PC or similar interface and offloaded via a communication gateway, typically via wi-fi when a train arrives at a station or at the maintenance plant. Data is sent to a Trenitalia data centre, and loaded into SAP HANA and cloud systems and dashboards for real-time monitoring, analysis and drill-down.

From ARC:

However, the team identified more representative KPI’s than mileage. These include door opening/closing cycles. They distinguished groups of components with higher or lower risk. With this information, Trenitalia is transitioning to a dynamic, component-based maintenance strategy in which higher risk components and components reaching the limits of their KPIs are checked and maintained more frequently; while other components are checked and maintained less frequently. In some cases, diverging KPIs of components on the same train can be balanced by choosing specific destinations. For example, trips causing more left wheel rotations and accelerations can be balanced with destinations leading to more right wheel accelerations. Trenitalia had to make its integrated travel and maintenance schedules much more granular to achieve the desired massive increase in reliability and savings.

Read More of ARC’s Blog Series >>

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

135

7:12 pm
January 25, 2017
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White Paper | Digital Prescriptive Maintenance

170125prescriptiveThe first wave of IIoT industry reports, about two years ago, included big claims and produced a lot of head scratching by manufacturers and Original Equipment Manufacturers (OEMs) due to the lack of actual applications. Two years later, pilot projects are producing results and next discipline to catch on is prescriptive maintenance.

The best way to describe prescriptive maintenance is “preventive maintenance technology with built-in intelligence, with the ultimate goal to minimize machine downtime. Below is a white paper, titled, “Digital Prescriptive Maintenance,” and it shows how sensors and diagnostics play their part with a prescriptive maintenance approach.

From the White Paper:

Prescriptive maintenance goes beyond the realm of productive, preventive, and predictive maintenance. Descriptive focuses on what happened in the past. Predictive analytics discovers potential options for the future. Prescriptive maintenance leverages all these approaches and capabilities. The realm of what should happen and the execution of optimized maintenance strategies is precisely the realm of prescriptive maintenance. With prescriptive maintenance, devices, in collaboration with operators, are proactive participants in their own maintenance.

>> Download the White Paper

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

76

10:54 pm
January 17, 2017
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National Instruments Continues IIoT Partnership Strategy in 2017

170117nilab001
Back in October 2016, National Instruments announced a partnership with Spark Cognition that provides a more holistic solution to asset management in the industrial machinery space. Now, National Instruments (NI) announces the opening of the new NI Industrial IoT Lab at its Austin headquarters.

According to NI, the IoT Lab will focus on intelligent systems that connect operational technology, information technology and the companies working on these systems. Examples cited include microgrid control and communication, advanced control for manufacturing, and asset monitoring for heavy equipment.

>> Related Content | National Instruments Partners with SparkCognition as IIoT Transformation Matures

Plus, interoperability is on NI’s mind as it moves forward in collaborating with protocol associations and technology leaders.

From the press release:

In this space, companies with expertise in communications protocols, controller hardware, I/O components, processing elements and software platforms come together to validate end-to-end solutions that can dramatically change the way businesses operate. Companies sponsoring the NI Industrial IoT Lab include: Analog Devices Inc, Avnu Alliance, Cisco Systems, Hewlett Packard Enterprise, Industrial Internet Consortium, Intel, Kalypso, OPC Foundation, OSIsoft, PTC, Real-Time Innovations, SparkCognition, Semikron, Viewpoint Systems and Xilinx.

“We are excited to strengthen partnerships with other world-class technology companies. A working showcase for new technologies can help all companies involved drive breakthrough innovations for utility grids, manufacturing, asset health monitoring and several other industries,” said Jamie Smith, business and technology director at NI.

To learn more about the new NI Industrial IoT Lab, please visit www.ni.com/iiot-lab.

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

22

8:15 pm
January 9, 2017
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Webinar | Operationalizing Analytics and IT


In this deep dive webinar on enterprise analytics, titled, “Operationalizing Analytics and IT,” Gahl Berkooz, chief analytics for General Motors Connected Customer Experience Division, locks into a discussion on the different approaches for managing analytical functions. Crucial foundations are being set as more than “half of manufacturers are using IIoT sensors and related technology for at least a year now,” according to a 2016 Genpact Study.

In this webinar, Berkhooz discusses centralized and decentralized data analytics, how data obliterates business silos and what approach works best. Berkhooz also identified four organizational structures that different companies are using in a HBR recent article.

Four Organizational Data Approaches:

  • A stand-alone data and analytics service function
  • An integrated operational data and analytics function
  • Data and analytics integrated as part of IT
  • Data and analytics embedded in operating divisions

Related Content | New IIoT Survey Reveals Management’s Readiness for Deployment

105

4:44 pm
January 3, 2017
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White Paper | ROI and the Connected Enterprise

170103itcwp2016 is done and internal debates with manufacturers and OEMs point to building business cases for Industrial Internet of Things (IIoT) initiatives. IT and OT suppliers are partnering to provide more holistic solutions for manufacturers, but internal metrics have to be in place for these new IIoT initiatives to be successful.

A new white paper from ThingWorx, a PTC company, “Quantifying the Return on Investment (ROI),” provides starting points for manufacturers on what key metrics are needed for measuring these projects. The paper includes three case applications and a deep dive into the business entities within an enterprise, such as assets, engineering. operations, services and sales.

>> Related Content | Partnerships Emerge as Manufacturers Eye IIoT Strategies 

The paper emphasizes a holistic look at IIoT and how the above entities are connected. For example, the first customer success story reveals these metrics from disparate business units: reduced mean time to repair (MTTR), reduced travel time for calls and a look at service calls for each problem resolved remotely.

From the white paper:

ThingWorx has interviewed customers, analyzed results, and found top- and bottom-line impacts that executives need to understand. The following sections share these finding and discuss what they mean for the enterprise. You will find an overview of the business metrics for IoT and the description of a framework to quantify the return on investment.

>> Click here to download the white paper

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 

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